Adaptive weighted fuzzy ILC for personalized FES trajectory reconstruction using an anatomical prior
Year: 2026
Type: article
Abstract: Objective . The clinical translation of closed-loop functional electrical stimulation (FES) systems is hindered by three interrelated bottlenecks: the trade-off between algorithmic complexity and embedded real-time feasibility, inefficient personalization, and a lack of empirical validation of human–machine cooperative performance in stroke patients. Approach . We propose a physiology-informed adaptive weighted fuzzy iterative learning control (AW-FILC) algorithm, implemented on a custom STM32F407-based embedded FES platform. The core innovation is a dual-layer adaptive mechanism that incorporates subcutaneous fat thickness T s , which is a key biophysical determinant of current attenuation measurable, as a feedforward prior. This prior is fused with real-time kinematic error feedback to dynamically modulate the learning gain and penalty factor. Main results . In simulations across two heterogeneous musculoskeletal models, AW-FILC achieves convergence within 5–7 iterations, with steady-state tracking accuracy of 10 –5 for normalized torque and < 0.13 ∘ for joint angles, outperforming PID-ILC and Type-1/Type-2 fuzzy ILC. In healthy participants ( n = 6 ), AW-FILC combined with voluntary effort achieved a trajectory tracking Pearson correlation of 0.87 ± 0.04, substantially exceeding the pure-FES ceiling (0.80) and approaching the physiological gold standard of natural bilateral coordination (0.984 ± 0.014). In stroke patients ( n = 8 ), AW-FILC elevated tracking correlation from 0.24 (pure open-loop FES) to 0.30 ± 0.02. This modest numerical gain represents a qualitative shift from pathological toward normalized movement patterns. The embedded implementation achieves per-iteration execution < 2 ms on STM32F407, and the system respects rather than quantifies patient voluntary effort, aligning with activity-dependent neuroplasticity principles. Significance . This work provides a computationally efficient, physiologically adaptive control core for embedded FES devices and empirically validates that AW-FILC-driven active rehabilitation bridges the gap between pathological and physiological coordination.
Source: Journal of Neural Engineering
Authors: Shiqin Liu, Jiao Wu, Weihua He, Xueliang Bao, Yu Zhou
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,325
OpenAlex ID: W7204465802
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